Field of the Disclosure
The present disclosure relates generally to artificial neuron networks and more particularly in one exemplary aspect to computerized apparatus and methods for implementing plasticity in spiking neuron networks.
Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in commonly owned U.S. patent application Ser. No. 13/152,084 entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION” filed Jun. 2, 2011, and U.S. patent application Ser. No. 13/152,119, filed Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, the foregoing each being incorporated herein by reference in its entirety.
Typically, artificial spiking neural networks, such as the exemplary network described in commonly owned and co-pending U.S. patent application Ser. No. 13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORK APPARATUS AND METHODS” filed Jun. 3, 2012, incorporated herein by reference in its entirety, may comprise a plurality of units (or nodes), which can be thought of as corresponding to neurons in a biological neural network. Any given unit may be connected to one or many other units via connections, also referred to as communications channels, and/or synaptic connections. The units providing inputs to any given unit are commonly referred to as the pre-synaptic units, while the units receiving the inputs are referred to as the post-synaptic units.
Individual ones of the unit-to-unit connections may be assigned, inter alga, a connection efficacy, which in general may refer to a magnitude and/or probability of input spike influence on unit output response (e.g., output spike generation/firing). The efficacy may comprise, for example a parameter (e.g., synaptic weight) by which one or more state variables of post-synaptic unit are changed. The efficacy may comprise a latency parameter by characterizing propagation delay from a pre-synaptic unit to a post-synaptic unit. In some implementations, greater efficacy may correspond to a shorter latency.
Some existing implementations of learning (e.g., slow feature analysis) by spiking neural networks via spike timing dependent plasticity and/or increased excitability may produce connection efficacy that is either too strong (e.g., one on a scale from 0 to 1) or too weak (e.g., zero). Some existing plasticity rules may employ a priori caps (e.g., hard limits) on efficacy magnitude and/or utilize manual tuning during network operation. Efficacy constraints may impede network response to varying inputs, while manual tuning may prevent network autonomous operation.
Accordingly, methods and apparatus for implementing plasticity in spiking networks are needed which, inter alia, overcome the aforementioned disabilities.